Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study
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OPEN Fall predictors beyond fall risk
assessment tool items for acute
hospitalized older adults:
a matched case–control study
Hye‑Mi Noh1, Hong Ji Song1, Yong Soon Park2, Junhee Han3 & Yong Kyun Roh4*
We investigated whether clinical factors including comorbidities, medications, and laboratory
results predict inpatient fall risk in older adults. The participants in this case–control study included
hospitalized older adults with acute conditions who had falls during their hospital stay (case group)
and 410 hospitalized older adults who did not experience falls (control group). Data on medical
history, fall risk assessment (Morse Fall Scale; MFS), medications, and laboratory results were
obtained. Conditional logistic regression analysis was performed to estimate the association between
clinical factors and falls. Receiver operating characteristic curves and area under the curve (AUC)
were used to determine whether clinical factors could discriminate between fallers and controls. We
evaluated three models: (M1) MFS, (M2) M1 plus age, sex, ward, and polypharmacy, and (M3) M2 plus
clinical factors. Patients with diabetes mellitus or MFS scores ≥ 45 had the highest risk of falls. Calcium
channel blockers, diuretics, anticonvulsants, and benzodiazepines were associated with high fall risk.
The AUC of the three models was 0.615, 0.646, and 0.725, respectively (M1 vs. M2, P = 0.042 and M2
vs. M3, P < .001). Examining clinical factors led to significant improvements in fall prediction beyond
that of the MFS in hospitalized older adults.
Inpatient falls are serious adverse events that increase the length of hospital stays, medical costs, and m ortality1.
It has been reported that advanced age is a risk factor for falls, with higher rates of falls and injurious falls among
older hospitalized patients than their younger counterparts2. To prevent older patients from accidentally falling
while hospitalized, it is necessary to assess the risk of falls and accordingly provide the necessary interventions.
Several fall risk assessment tools such as the Morse Fall Scale (MFS)3, St Thomas’s Risk Assessment Tool in Falling
Elderly Inpatients4, and the Hendrich Fall Risk Model have been utilized in hospitals5. Of these, the MFS is the
most popular because of its ease of use; nurses can perform the rating in under three minutes. The MFS consists
of six items, each of which is scored between either 0–15 points or 0–30 points: history of falling within three
months, secondary diagnosis, intravenous (IV) therapy/heparin lock, use of ambulatory aid, gait, and mental
status. Patients with total MFS scores ≥ 45 are defined as high-risk6. However, the models’ prediction abilities
vary when applied in hospitalized patients with acute c onditions7, and they lack consideration of various clini-
cal factors. In acute care hospitals, patients’ medications and medical conditions can change rapidly according
to treatment. This indicates that adding factors such as comorbidities, fall risk-increasing drugs (FRID), and
laboratory results to previous tools may improve their ability to predict inpatient falls. Recently, the Agency
for Healthcare Research and Quality (AHRQ) suggested that evaluating medications, disease states, laboratory
results, and patients’ education levels can prevent falls in h ospitals8.
Furthermore, studies have reported that certain medications increase the risk of inpatient falls in hospitalized
patients with acute conditions. A matched case–control study at a tertiary care center in the US reported that
psychotropic agents such as benzodiazepines, antiepileptics, antidepressants, antipsychotics, hypnotics, central
nervous system stimulants, narcotics, and antiparkinsonian agents were associated with the risk of injurious falls
in the inpatient s etting9. A retrospective cohort study in two US hospitals demonstrated that a higher number of
1
Department of Family Medicine, College of Medicine, Hallym University Sacred Heart Hospital, Hallym University,
Anyang 14068, Republic of Korea. 2Department of Family Medicine, Chuncheon Sacred Heart Hospital, Hallym
University College of Medicine, Chuncheon 24253, Republic of Korea. 3Department of Statistics and Institute of
Statistics, Hallym University, Chuncheon 24252, Republic of Korea. 4Department of Family Medicine, Kangnam
Sacred Heart Hospital, 1, Singil‑ro, Yeongdeungpo‑gu, Seoul 07441, Republic of Korea. *email: rohyk@
hallym.ac.kr
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FRID, higher comorbidity predisposition including 11 past diagnoses related to falls (confusion, disorientation,
and impulsivity; dizziness and vertigo; hallucinations; visual impairment; hearing loss; vestibular dysfunction;
language impairment; orthostatic hypotension; cerebrovascular accident; Parkinson’s disease; and seizure dis-
order), and a history of falling increased the risk of inpatient falls10.
In general, there has been little research focusing on older hospitalized patients, although one German study
reported that benzodiazepines, serotonin-noradrenalin reuptake inhibitors, and Z-drugs (zopiclone, eszopiclone,
zaleplon, and zolpidem) were associated with inpatient falls among older adults (aged ≥ 65 years). Additionally,
they demonstrated that the presence of hyponatremia and leukocytosis on admission increased the risk of f alls11.
Other studies have reported that abnormal laboratory values such as those indicating anemia or hyponatremia
are associated with inpatient falls12,13.
Considering these recent findings, we investigated whether clinical factors including comorbidities, FRID, and
laboratory results before falls were associated with inpatient falls. We extracted comprehensive clinical data from
a clinical data warehouse (CDW) of electronic health records and evaluated the risk-discriminative performance
between MFS alone and MFS plus clinical factors. We used a case–control study design that matched age, admis-
sion date, ward, and length of stay before falls among older hospitalized patients in a Korean tertiary hospital.
Methods
Study population. The study took place at an academic tertiary hospital in a city in Korea. The hospital
has 13 ward units and 710 inpatient acute care beds. The facility provides medical services in 33 departments
including internal medicine, family medicine, general surgery, neurology, orthopedics, urology, otorhinolaryn-
gology, ophthalmology, pediatrics, psychiatrics, and obstetrics and gynecology. All study participants provided
informed consent, and the institutional review board of Hallym University Sacred Heart Hospital (approval
number 2018–11-002–001) reviewed and approved the research protocol. All procedures were performed in
accordance with the Declaration of Helsinki.
Fallers and control group. This study used a retrospective case–control design. Falls were identified by
the adverse event reports which filled out by the nurses of the department on electronic database. From Janu-
ary 2016 through December 2018, 369 inpatient falls occurred in the study hospital. During the same period,
783,758 beds were occupied, and the fall rate was 4.71 falls per 1,000 occupied bed days. For this study, we
included 269 inpatient falls among older patients. If a patient had multiple falls during their hospital stay, we
only included data for the first fall. In this manner, 251 fallers were identified. We excluded patients admitted to
psychiatric wards (n = 23) and those who did not match the control characteristics (n = 18), resulting in 210 fall-
ers being eligible for this study. The CDW of electronic health records identified one or two controls for each case
from those patients who did not experience falls during their hospital stay. Our institution has been using smart
CDW since 2016 to analyze big data based on the QlikView Elite Solution (Qlik, Radnor, Pennsylvania, USA)14.
Matching characteristics identified were age (within two years), admission date (within two weeks), ward, and
length of stay before falls. Finally, 210 fallers (57–95 years) and 410 controls (55–97 years) were included in the
study.
Data collection. From the CDW, we extracted data on patients’ demographics, medical histories, fall risk
assessments, medications, and laboratory results. At admission, nurses interviewed the patients to obtain their
demographic data including smoking status, alcohol consumption, and presence of comorbidities (hyperten-
sion, diabetes mellitus, dyslipidemia, heart disease, cerebrovascular disease, malignancy, chronic lung disease,
chronic kidney disease, chronic liver disease, osteoporosis, cognitive impairment, Parkinson’s disease, psychi-
atric disease, urinary disorder, benign prostate hyperplasia, and visual or hearing impairment). Body weight
(kg) and height (cm) were measured to the nearest 0.1 kg and 0.1 cm. Body mass index (BMI) was calculated as
weight divided by height squared (kg/m2).
We compared medications including FRID that were used one and two days before falls in fallers with those
in the control group on matched hospital days. FRID was defined based on the AHRQ Fall Prevention T oolkit8
15
and the American Geriatric Society Beers criteria . Beta blockers, calcium channel blockers, angiotensin II
receptor antagonists, angiotensin-converting enzyme inhibitors, digoxin, loop diuretics, other diuretics (thiazides
or spironolactone), alpha blockers, anticholinergics, antihistamines, weak opioids, opioids, antipsychotics, anti-
convulsants, benzodiazepine, hypnotics, anti-dementia drugs, antiparkinsonians, selective serotonin reuptake
inhibitors or serotonin-norepinephrine reuptake inhibitors, tricyclic antidepressants, other antidepressants,
vasodilating agents, and muscle relaxants were included in FRIDs.
In the study hospital, nurses used the MFS to perform fall-risk assessments at least every two days or whenever
a patient’s medical condition changed based on electronic health records. The MFS consists of six items: three
with possible answers of ‘yes” or “no” (history of falling within three months, secondary diagnosis, IV therapy/
heparin lock); ambulatory aid use with possible answers of “none,” “bed rest,” “nurse assist/crutches,” “cane,” or
“walker/furniture”; gait impairment with possible answers of “normal,” “bed rest,” “immobile/weak gait/impaired
gait,” and mental status, with possible answers of “oriented to own ability” or “forgets limitations.” Items scores
range between 0–15 points and 0–30 points and total MFS scores range from 0 to 125 points. The high-risk
group was defined as patients with total MFS scores ≥ 4516. Laboratory tests included complete blood count,
blood urea nitrogen, creatinine, serum electrolytes, glucose, hemoglobin A1c, aspartate aminotransferase (AST),
alanine aminotransferase (ALT), protein, and albumin. Abnormal laboratory values were defined as follows:
leukocytosis (white blood cell count > 10,000/uL), anemia (hemoglobin < 13 g/dL in men or < 12 g/dL in women),
hypoalbuminemia (albumin < 3.8 g/dL), decreased estimated glomerular filtration rate (< 60 mL/(min*1.73 m2)),
hyponatremia (sodium < 135 mmol/L), hypokalemia (potassium < 3.6 mmol/L), abnormal liver function test (≥ 2*
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Fallers Controls
(n = 210) (n = 410) P value
Age, years 73.7 ± 8.41 73.6 ± 8.41 0.965
Male, n (%) 125 (59.5%) 240 (58.5%) 0.813
Height, cm 159.9 ± 9.82 160.3 ± 9.16 0.793
Body weight, kg 58.4 ± 11.66 60.5 ± 11.50 0.009
Body mass index, kg/m2 22.8 ± 3.87 23.5 ± 3.69 0.009
Smoking, n (%) 28 (13.3%) 34 (8.3%) 0.087
Alcohol consumption, n (%) 27 (12.9%) 54 (13.2%) 0.913
Comorbidities
Hypertension 123 (58.6%) 224 (54.6%) 0.35
Diabetes mellitus 79 (37.6%) 112 (27.3%) 0.009
Dyslipidemia 19 (9.0%) 43 (10.5%) 0.572
Heart disease 33 (15.7%) 60 (14.6%) 0.721
Cerebrovascular disease 22 (10.5%) 47 (11.5%) 0.711
History of malignancy 53 (25.2%) 103 (25.1%) 0.975
Chronic lung disease 16 (7.9%) 27 (6.6%) 0.632
History of tuberculosis 12 (5.7%) 20 (4.9%) 0.656
Chronic kidney disease 14 (6.7%) 29 (7.1%) 0.85
Chronic liver disease 8 (3.8%) 9 (2.2%) 0.244
Osteoporosis 8 (3.8%) 29 (7.1%) 0.104
Cognitive impairment 14 (6.7%) 11 (2.7%) 0.017
Parkinson’s disease 8 (3.8%) 6 (1.5%) 0.063
Psychiatric disease 8 (3.8%) 15 (3.7%) 0.925
Benign prostate hyperplasia* 16 (12.8%) 38 (15.8%) 0.491
Number of comorbidities 0.418
0 18 (8.6%) 47 (11.5%)
1 112 (53.3%) 223 (54.4%)
≥2 80 (38.1%) 140 (34.1%)
Visual impairment 56 (26.7%) 98 (23.9%) 0.451
Hearing impairment 18 (8.6%) 37 (9.0%) 0.851
Sleep disorder 13 (6.2%) 26 (6.3%) 0.596
Length of hospital stay 19.5 ± 18.8 11.7 ± 9.53 < 0.001
Discharge disposition < 0.001
Home 137 (65.2%) 343 (83.7%)
Hospital transfer 63 (30.0%) 46 (11.2%)
Death 9 (4.3%) 10 (2.4%)
Table 1. General characteristics of fallers and matched controls. Data are presented as means (standard
deviation) or frequencies (percentage), as appropriate. *The prevalence of benign prostate hyperplasia was
calculated in male subjects.
upper normal limit of AST or ALT), and uncontrolled diabetes mellitus (hemoglobin A1c ≥ 8.0%). We compared
MFS score and laboratory values that were obtained immediately prior to falls in the case group with those in
the control group on matched hospital days.
Statistical analysis. Mean (standard deviation) or frequency (percentage) was used to describe patients’
general characteristics. The Student’s t-test or Mann–Whitney U test was used to compare continuous vari-
ables, and either the chi-square test or Fisher’s exact test was used to compare categorical variables. We esti-
mated odds ratios (OR) and 95% confidence intervals (CIs) to investigate the association between clinical factors
and inpatient falls using conditional logistic regression analysis. All variables that were significantly different
between fallers and controls, such as BMI, diabetes mellitus, cognitive impairment, MFS score, leukocytosis,
hypoalbuminemia, hyponatremia, and polypharmacy (concomitant use of five or more drugs), use of calcium
channel blockers, diuretics (thiazides or spironolactone), antipsychotics, anticonvulsants, benzodiazepines, and
antiparkinsonians, were included in the model. We evaluated matching covariate-adjusted receiver operating
characteristic (ROC) curves and area under the curve (AUC) to determine whether clinical factors could dis-
criminate between fallers and controls17. We evaluated three models: (M1) MFS alone, (M2) MFS plus matching
covariates (age and ward), sex, and polypharmacy, (M3) M2 plus laboratory values, FRID, and comorbidities.
Non-parametric methods were applied according to DeLong et al.18 to evaluate differences in AUC estimates.
All statistical tests were two-sided, and the significance level was set at P < 0.05. All analyses were performed
using IBM SPSS Statistics for Windows version 21.0 (IBM Corp., Armonk, NY, USA) and R Studio version 3.6.1.
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Fallers Controls
(n = 210) (n = 410) P-value
Morse Fall Scale (score) 39.3 ± 22.64 30.4 ± 21.12 < 0.001
Assessment of fall risk < 0.001
High risk (≥ 45) 105 (50.0%) 131 (32.0%)
Low risk (< 45) 105 (50.0%) 279 (68.0%)
Item of Morse Fall Scale
History of falling within 3 months 30 (14.3%) 27 (6.6%) 0.002
Secondary diagnosis 61 (29.0%) 107 (26.1%) 0.434
IV therapy/heparin lock 146 (69.5%) 256 (62.4%) 0.08
Ambulatory aid
Crutches, cane, walker 88 (41.9%) 116 (28.3%) 0.001
Furniture 16 (7.6%) 21 (5.1%)
Gait
Weak gait 130 (61.9%) 191 (46.6%) 0.001
Impaired gait 4 (1.9%) 13 (3.2%)
Mental status (forgets limitation) 33 (15.7%) 36 (8.8%) 0.009
Laboratory results
Leukocytosis (> 10,000/uL) 47 (22.4%) 54 (13.2%) 0.003
Anemia (men < 13 g/dL, women < 12 g/dL) 144 (68.6%) 270 (65.9%) 0.457
Hypoalbuminemia (< 3.8 g/dL) 115 (54.8%) 190 (46.3%) 0.047
2))
Decreased eGFR (< 60 mL/ (min*1.73 m 47 (22.4%) 95 (23.2%) 0.825
Hyponatremia (< 135 mmol/L) 64 (30.5%) 90 (22.0%) 0.020
Hypokalemia (< 3.6 mmol/L) 34 (16.2%) 49 (12.0%) 0.142
Abnormal liver function test (≥ Upper normal limit*2) 17 (8.1%) 35 (8.5%) 0.851
Uncontrolled diabetes (Hba1c ≥ 8.0%) 19 (9.0%) 24 (5.9%) 0.138
Table 2. Fall risk assessment and laboratory results of fallers and matched controls. Data are presented as
means (standard deviation) or frequencies (percentage), as appropriate.
Results
General characteristics of fallers and controls. The age range of the sample was 55–97 years, and the
mean age and median age was 73.7 years and 73.0 years, respectively. Comparisons of the general characteristics
of fallers and controls included age, sex, height, body weight, BMI, and whether they smoked or drank alcohol.
These and the list of comorbidities are shown in Table 1. There were no significant differences in age, sex, number
of comorbidities, and visual or hearing impairment between the two groups. Fallers had lower BMI compared
to controls (22.8 ± 3.87 and 23.5 ± 3.69, respectively, P = 0.009). The prevalence of diabetes mellitus and cogni-
tive impairment was higher among fallers than controls (37.6% and 27.3%, P = 0.009; 6.7% and 2.7%, P = 0.017;
respectively). In addition, fallers had longer hospital days and lower rates of home discharge compared to con-
trols (all P < 0.001).
Comparison of fall risk assessment and laboratory results between fallers and controls. Table 2
shows the comparison of fall risk assessment and laboratory results between the groups. Mean scores of MFS
items were higher among fallers than controls (39.3 and 30.4, respectively, P < 0.001). Fifty percent of fallers
and 32% of controls had a high risk of falling. Among the six items of the MFS, more fallers than controls had
a history of falling, ambulatory aid, gait impairment, and altered mental status (P = 0.002, P = 0.001, P = 0.001,
and P = 0.009). Fallers had higher percentages of leukocytosis, hypoalbuminemia, and hyponatremia compared
to controls (22.4% and 13.2%, P = 0.003; 54.8% and 46.3%, P = 0.047; 30.5% and 22.0%, P = 0.020; respectively).
Comparison of medications and FRID between fallers and controls. Table 3 shows the compari-
son of medications and FRID between the groups. Both number of total medications and FRID were higher in
fallers than controls (all P < 0.001). Among FRID, calcium channel blockers, diuretics (thiazides or spironolac-
tone), antipsychotics, anticonvulsants, benzodiazepine, and antiparkinsonians were more commonly taken by
fallers than controls (21.9% and 12.0%, P = 0.001; 10% and 4.4%, P = 0.006; 17.1% and 11.0%, P = 0.031; 26.2%
and 12.0%, P < 0.001; 10.0% and 4.1%, P = 0.004; 8.6% and 4.4%, P = 0.004; and 3.9% and 0.7%, P = 0.009; respec-
tively). Fallers had higher polypharmacy compared to controls (79.5% and 65.9%, respectively, P < 0.001). How-
ever, there was no significant difference between the groups based on medication changes on the day before falls
(P = 0.099).
Conditional logistic regression analysis of clinical factors and falls. Table 4 presents the associa-
tion between clinical factors and falls. Using conditional logistic regression analysis, ORs indicated that older
adults with diabetes mellitus and high fall risk as assessed by the MFS had 1.76-fold (95% CI 1.12–2.75) and
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Fallers Controls
(n = 210) (n = 410) P value
Number of medications 8.5 ± 4.55 6.6 ± 4.37 < 0.001
Number of FRID 2.6 ± 2.10 1.8 ± 1.87 < 0.001
FRID
Digoxin 6 (2.9%) 8 (2.0%) 0.472
Beta blockers 29 (13.8%) 44 (10.7%) 0.26
Calcium channel blockers 46 (21.9%) 49 (12.0%) 0.001
ACE inhibitors or ARBs 46 (21.9%) 70 (17.1%) 0.158
Loop diuretics 33 (15.7%) 48 (11.7%) 0.161
Diuretics (thiazides or spironolactone) 21 (10.0%) 18 (4.4%) 0.006
Alpha blockers 26 (12.4%) 56 (13.7%) 0.657
Anticholinergics 24 (11.4%) 46 (11.2%) 0.938
Antihistamines 18 (8.6%) 25 (6.1%) 0.251
Weak opioids 40 (19.0%) 77 (18.8%) 0.936
Opioids 24 (11.4%) 51 (12.4%) 0.715
Antipsychotics 36 (17.1%) 45 (11.0%) 0.031
Anticonvulsants 55 (26.2%) 49 (12.0%) < 0.001
Benzodiazepines 21 (10.0%) 17 (4.1%) 0.004
Hypnotics 7 (3.3%) 6 (1.5%) 0.124
Anti-dementia drugs 7 (3.3%) 9 (2.2%) 0.398
Antiparkinsonians 8 (3.9%) 3 (0.7%) 0.009
SSRIs or SNRIs 14 (6.7%) 18 (4.4%) 0.225
TCAs 8 (3.8%) 10 (2.4%) 0.336
Other antidepressants 5 (2.4%) 9 (2.2%) 0.883
Muscle relaxants 15 (7.1%) 19 (4.6%) 0.194
Vasodilating agents 14 (6.7%) 22 (5.4%) 0.512
Polypharmacy (≥ 5 medications) 167 (79.5%) 270 (65.9%) < 0.001
Medication change on the day before falls 0.099
Inclusion of FRID 76 (36.2%) 115 (28.0%)
Withdrawal of FRID 15 (7.14%) 36 (8.78%)
Table 3. Fall risk-increasing drugs prescribed to fallers and matched controls. Data are means (standard
deviation) or frequencies (percentage), as appropriate. ACE inhibitors, angiotensin-converting enzyme
inhibitors; ARBs, angiotensin II receptor Antagonists; FRID, fall risk-increasing drugs; SSRIs, selective
serotonin reuptake inhibitors; SNRIs, serotonin norepinephrine re-uptake inhibitors; TCAs; tricyclic
antidepressants.
2.23-fold (95% CI 1.44–3.44) higher risk of falls, respectively. ORs also indicated that among older adults pre-
scribed FRIDs, those taking calcium channel blockers, diuretics (thiazides or spironolactone), anticonvulsants,
and benzodiazepines had higher fall risks by 1.71 times (95% CI 1.01–2.88), 2.24 times (95% CI 1.02–4.90), 3.04
times (95% CI 1.73–5.32), and 2.26 times (95% CI 1.05–4.85), respectively.
ROC curves of the clinical factors predicting falls. Figure 1 shows the comparisons of three ROC
curves of the clinical factors predicting falls. M1 was MFS only. Age, sex, ward, and polypharmacy were added
in M2, and diabetes mellitus, cognitive impairment, leukocytosis, hypoalbuminemia, hyponatremia, calcium
channel blocker, diuretics (thiazides or spironolactone), antipsychotics, anticonvulsants, benzodiazepines,
and antiparkinsonians were added in M3. The AUC (95% CI) of each model was 0.615 (0.568–0.662), 0.646
(0.601–0.692), and 0.725 (0.683–0.766), respectively. There were significant improvements in discrimination
between fall cases and controls when clinical factors were combined with MFS (M1 vs. M2, P = 0.042; M2 vs.
M3, P < 0.001, respectively).
Discussion
In this matched case–control study, we demonstrated that several clinical factors also identified in other recent
studies were associated with inpatient falls. After adjusting for the MFS, diabetes mellitus and FRID, including
calcium channel blockers, diuretics (thiazides or spironolactone), anticonvulsants, and benzodiazepines, were
significantly associated with higher fall risk. Additionally, we identified significant improvements in the accuracy
of fall risk prediction when clinical factors were combined with MFS.
In our study, diabetes mellitus was associated with the risk of falling. A previous study based on data from
the longitudinal Health, Aging, and Body Composition Study reported that older adults with diabetes mellitus
had higher risks of injurious falls19. Risk factors for falls including peripheral neuropathy, visual impairment,
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Fall
OR (95% CI)
Body mass index, kg/m2 0.97 (0.92–1.03)
Comorbidities
Diabetes mellitus 1.76 (1.12–2.75)
Cognitive impairment 2.91 (0.97–8.70)
Morse Fall Scale assessment
High risk (≥ 45) 2.23 (1.44–3.44)
Low risk (< 45) 1
Laboratory result
Leukocytosis 1.37 (0.81–2.32)
Hypoalbuminemia 1.37 (0.90–2.09)
Hyponatremia 1.45 (0.92–2.28)
Fall risk-increasing drugs
Calcium channel blockers 1.71 (1.01–2.88)
Diuretics (thiazides or spironolactone) 2.24 (1.02–4.90)
Antipsychotics 1.25 (0.68–2.28)
Anticonvulsants 3.04 (1.73–5.32)
Benzodiazepines 2.26 (1.05–4.85)
Antiparkinsonians 2.80 (0.68–11.49)
Table 4. Conditional logistic regression analysis of fall-related clinical factors. Odds ratio (OR) and 95%
confidence interval (CI) by conditional logistic regression analysis.
and decreased physical performance are more common in patients diagnosed as diabetic20. Our results concern-
ing older patients showed that the prevalence of cognitive impairment was also higher in fallers than controls,
although there was no significant association identified by conditional logistic regression analysis. Older adults
experiencing dementia were also at a higher risk of falling21. This makes sense in view of a systematic review
and meta-analysis that reported that cognitive impairment was associated with falls, injurious falls, and distal
radius fractures22. Furthermore, in older adults with dementia, the decline of motor and executive function,
neuropsychiatric symptoms, and related medication use were associated with fall r isk23,24.
Among FRID, in our study, calcium channel blockers, diuretics excluding loop diuretics, anticonvulsants,
and benzodiazepines were significantly associated with fall risk. The association of calcium channel blockers
and diuretics with fall risk is controversial. A recent systematic review and meta-analysis including five studies
reported that taking calcium channel blockers was not associated with fall risk (OR 1.00, 95% CI 0.80–1.24)25.
However, among the five studies, one that was conducted with patients in acute care hospitals reported that
calcium channel blockers were significantly associated with fall risk (OR 2.45, 95% CI 1.20–5.01)26, which was
consistent with our findings. Older adults commonly use diuretics as antihypertensives and for treating and
preventing heart failure as well as ascites in liver cirrhosis. However, they can cause orthostatic hypotension,
dizziness, and muscle weakness related to hyponatremia, which lead to increased risks of falls, and, in our study,
diuretics excluding loop diuretics were associated with increased fall risk. In fact, another population-based
case–control study reported that thiazides increased fall risk (OR 1.25, 95% CI 1.15–1.36)27, in line with our
findings. However, recent systematic reviews and meta-analyses have also reported that loop diuretics are associ-
ated with fall risk, whereas thiazides are not28. Therefore, there is a need for further studies to clarify the actual
fall risk associated with this drug in hospitalized older adults.
We also noted that many of the older adults in our study used anticonvulsants for treating epilepsy, mood
disorders, and neuropathic p ain29. Adverse reactions to antiepileptic drugs including dizziness, blurred vision,
and sedation are likely to increase the risk of falls, as demonstrated in a recent systematic review and meta-
analysis that reported an association between antiepileptic use and increased risk of falling in older a dults30. We
also looked at the use of benzodiazepines, and the results indicate that they are associated with higher fall risk,
which is in line with a previous systematic r eview31. Benzodiazepines can cause sedation and impaired balance
and gait, all of which lead to increases in the risk of falls among older adults32.
Our results also indicate that the prevalence of leukocytosis, hypoalbuminemia, and hyponatremia was higher
in fallers than controls. However, there was no significant association between laboratory results and fall risk
indicated by conditional logistic regression analysis. Hyponatremia is an electrolyte imbalance common in older
adults, which can lead to muscle weakness or cramps, lethargy, and confusion. It can be caused by the use of
diuretics and anticonvulsants, and comorbidities include heart failure, cirrhosis, and chronic kidney disease.
Previous studies have suggested that hyponatremia is a potential risk factor for falls13,32. Hypoalbuminemia, a
marker of poor nutritional status, is a possible risk factor for falling. Further, a recent study reported that in hos-
pitalized patients with hematologic diseases, hypoalbuminemia was associated with increased risks of inpatient
falls33. In French community-dwelling older adults, poor nutritional status, as assessed by the Mini Nutritional
Assessment, was associated with falls and fractures34. Additionally, leukocytosis, one of the signs of infection,
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1.00
0.75
name
sensitivity
model1
0.50
model2
model3
0.25
0.00
0.00 0.25 0.50 0.75 1.00
1 − specificity
Figure 1. Comparisons of receiver operating characteristic curves. Model 1, (M1) Morse Fall Scale (MFS);
Model 2, (M2) MFS, age, sex, ward, and polypharmacy; Model 3, (M3) MFS, age, sex, ward, polypharmacy,
diabetes mellitus, cognitive impairment, leukocytosis, hypoalbuminemia, hyponatremia, calcium channel
blockers, diuretics (thiazides or spironolactone), antipsychotics, anticonvulsants, benzodiazepines, and
antiparkinsonians.
might be associated with fall risk, as a previous study of German older hospitalized patients found that the pres-
ence of leukocytosis on admission was associated with f alls11.
This study had several strengths. First, we were able to identify various fall-related clinical factors before fall
events using the CDW. Use of the CDW allowed relatively short data collection times and ensured high data
quality35. In addition, we analyzed risk-discriminative performances between the MFS alone and MFS plus clini-
cal factors. Second, we focused on hospitalized older adults, a vulnerable population regarding both falls and
possible adverse reactions to FRID. However, our study also had some limitations. First, the setting was a single
Korean tertiary hospital, which might not be representative of the general population. Second, it is difficult to
completely distinguish the effects of comorbidities, related medications, and laboratory results. For example, it
is hard to sort out the effects of comorbidities from those of the related medications (cognitive impairment and
psychotropic agents) or the relationship of laboratory results and adverse reactions to medications (hyponatremia
and diuretics or anticonvulsants). Finally, although we considered comprehensive factors, the AUC of the final
Scientific Reports | (2021) 11:1503 | https://doi.org/10.1038/s41598-021-81034-9 7
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model was 0.726, which is not very high, indicating that there is a need for further research to identify other pos-
sible fall-related factors. Recently, machine learning algorithms using electronic health records have been evalu-
ated for predicting falls among older adults making emergency department v isits36. In the near future, developing
comprehensive machine learning models to predict inpatient falls based on electronic health records is needed.
In conclusion, we identified that several clinical factors were associated with higher risks of falls among older
adults hospitalized for acute care. Patients with a history of diabetes mellitus and the use of calcium channel
blockers, diuretics, anticonvulsants, and benzodiazepines were associated with higher fall risks. Adding these
clinical factors to the MFS led to significant improvements in fall prediction. Based on the results, we believe that
developing novel fall risk assessment tools reflecting the various clinical factors identified is necessary.
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on
reasonable request.
Received: 4 May 2020; Accepted: 4 January 2021
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Acknowledgements
The Hallym University Research Fund 2019 [Grant Number HURF-2019-32] supported this work.
Author contributions
H.-M.N. was involved in the conception and design of the study, acquisition of data and funding, statistical analy-
sis and interpretation of data, and drafting of the manuscript. H.J.S. and Y.S.P. were involved in the conception
and design of the study, critical revision of the manuscript, and supervision. J.H. was involved in the conception
and design of the study and statistical analysis. Y.K.R. was involved in the conception and design of the study,
interpretation of data, critical revision of the manuscript, and supervision.
Competing interests
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to Y.K.R.
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